This program was written to allow exploration of the foundations of the science of chaos. It is a mathematics laboratory designed to extend the mathematical modeling capability of our Order program. It provides a convenient way of experimenting with the concepts of chaos in the classroom or at home, allowing the student to gain real understanding of the subject. ChaosLab covers those topics where the mathematics of chaos most closely touches traditional high school or college freshman math.
The concepts accessible through ChaosLab include functions of real variables, functions of complex variables, convergence and divergence, iteration, point attractors, periodic attractors, chaotic attractors, fractals, Mandelbrot and Julia sets, functions as transforms and phase-control diagrams. There is extensive documentation in the form of tutorial lessons and the help files which enable ChaosLab to stand alone as a teaching and learning tool. Teachers may use ChaosLab to author lesson plans and leave them on a computer or network for students to use. Students may conduct experiments in ChaosLab and use the program to write and illustrate their reports. The program includes a parser allowing the user to write real and complex functions which may then be iterated as one dimensional maps, in phase-control space or in complex sets.
The program requires one of the 32 bit Windows operating systems, Win95/98/ME/2000/NT/XP, and 32 megabytes of RAM. The installed size of the program on the hard drive is about 1.5 megabytes. Click on the CLSetup link to download the installation program. Save it to a convenient location on your computer and run it to install ChaosLab.
Below are links to some screen shots of the program output.
This program is also ideal for people not in the education racket who are just curious about chaos and those striking images that are appearing on the world wide web, the jackets of books and T-shirts. ChaosLab can not only generate images from the usual examples of chaos, but also create wholly new examples literally never seen before.